https://orcid.org/0000-0003-4260-4294
https://orcid.org/0000-0002-7461-6364
https://orcid.org/0000-0003-3873-7983
https://orcid.org/0000-0001-9149-3813
https://orcid.org/0000-0002-6355-134X
https://orcid.org/0000-0001-8332-5860
https://orcid.org/0000-0001-9867-6196
https://orcid.org/0000-0003-4260-4294
https://orcid.org/0000-0002-7461-6364
https://orcid.org/0000-0003-3873-7983
https://orcid.org/0000-0001-9149-3813
https://orcid.org/0000-0002-6355-134X
https://orcid.org/0000-0001-8332-5860
https://orcid.org/0000-0001-9867-6196
The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable the deployment of automated driving technologies, due to the complex operational domain of AVs. One candidate is scenario-based assessment, in which test cases are derived from real-world road traffic scenarios obtained from driving data. Because of the high variety of the possible scenarios, using only observed scenarios for the assessment is not sufficient. Therefore, methods for generating additional scenarios are necessary. Our contribution is twofold. First, we propose a method to determine the parameters that describe the scenarios to a sufficient degree while relying less on strong assumptions on the parameters that characterize the scenarios. By estimating the probability density function (pdf) of these parameters, realistic parameter values can be generated. Second, we present the Scenario Representativeness (SR) metric based on the Wasserstein distance, which quantifies to what extent the scenarios with the generated parameter values are representative of real-world scenarios while covering the actual variety found in the real-world scenarios. A comparison of our proposed method with methods relying on assumptions of the scenario parameterization and pdf estimation shows that the proposed method can automatically determine the optimal scenario parameterization and pdf estimation. Furthermore, it is demonstrated that our SR metric can be used to choose the (number of) parameters that best describe a scenario. The presented method is promising, because the parameterization and pdf estimation can directly be applied to already available importance sampling strategies for accelerating the evaluation of AVs.
Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles
IEEE Transactions on Intelligent Transportation Systems ; 23 , 10 ; 18794-18807
01.10.2022
3347550 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| Tema Archiv | 2003
SCENARIO GENERATION DEVICE AND SCENARIO GENERATION METHOD
| Europäisches Patentamt | 2023
SCENARIO GENERATION DEVICE AND SCENARIO GENERATION METHOD
| Europäisches Patentamt | 2023